Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add leynos/visual-storytelling-skills --skill phoneticizegit clone --depth 1 https://github.com/leynos/visual-storytelling-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/leynos/visual-storytelling-skills/phoneticize)<a href="https://agentmods.dev/skills/leynos/visual-storytelling-skills/phoneticize"><img src="https://agentmods.dev/badge/skills/leynos/visual-storytelling-skills/phoneticize/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/leynos/visual-storytelling-skills/phoneticize"><img src="https://agentmods.dev/badge/skills/leynos/visual-storytelling-skills/phoneticize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00225 | $0.02652 |
| Opus 5 | $0.00112 | $0.01326 |
| Sonnet 5 | $0.00045 | $0.00530 |
| Haiku 4.5 | $0.00022 | $0.00265 |
Grade A, and why
phoneticize scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Phoneticize — TTS pronunciation prep
A workflow for identifying pronunciation hazards in a TTS script, agreeing phonetic renderings with the user via audio previews, and emitting a phoneticized script ready for narration.
Read first
| Reference | When to read | Path |
|---|---|---|
| Detection heuristics | Before Phase 1 — patterns that find candidates and how to combine them | references/detection-heuristics.md |
| Respelling conventions | Before Phase 2 — how to write phonetic respellings that Eleven v3 actually obeys | references/respelling-conventions.md |
| Eleven v3 format notes | Before Phase 3 and Phase 5 — what the engine accepts and silently ignores | references/eleven-v3-notes.md |
Governing principles
-
Preview the fragment, not the word. TTS prosody depends on surrounding context. A respelling that sounds right in isolation collapses inside a sentence. Render fragments throughout.
-
Respelling is the primary output, not SSML. Eleven v3 silently drops
<phoneme>tags (seereferences/eleven-v3-notes.md). Inline respelling —Siobhán→shi-VAWNdirectly in the prose — is what actually changes the model's output. IPA goes in the table for archival precision; respelling goes in the script. -
Mark uncertainty, never guess. A wrong respelling shipped with confidence is worse than an explicit
?the user resolves. When the pronunciation isn't obvious, ask. -
Stable IDs across iterations. Each candidate gets a row ID (
P01,P02, …) on first pass and keeps it for the lifetime of the table. The user references rows by ID; regenerate only the rows that changed. -
Render once, accept once. Never re-render an
acceptedrow — it wastes Higgsfield calls and the user will assume something broke when the audio differs subtly between takes.
Phase 1 — Scan
Combine the regex helper with semantic reading. Neither alone catches everything: regex misses ordinary-looking words with non-obvious pronunciation (Worcester, Featherstonehaugh), and an LLM scan alone will miss tokens deep in long scripts.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 255 lines · 225 tokens per session scan A 897545d4de61
phoneticize is a skill published in the GitHub repository leynos/visual-storytelling-skills (6 stars, last pushed 1mo ago), licensed ISC. It adds 225 tokens to every session and 2,652 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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